Sales Escalation Report

Sales Data Summary

This report summarizes the sales data for the selected time period.

# Sample data for sales (make sure this is the filtered data from Shiny in a real app)
sales_data <- data.frame(
  date = seq.Date(from = as.Date("2024-01-01"), by = "month", length.out = 25),
  sales = c(100, 110, 115, 120, 125, 130, 125, 135, 140, 145, 150, 155, 160, 165, 170, 
            180, 175, 185, 190, 195, 200, 210, 220, 215, 230)
)

# Display summary of the sales data
summary(sales_data)

##       date                sales      
##  Min.   :2024-01-01   Min.   :100.0  
##  1st Qu.:2024-07-01   1st Qu.:130.0  
##  Median :2025-01-01   Median :160.0  
##  Mean   :2024-12-31   Mean   :161.8  
##  3rd Qu.:2025-07-01   3rd Qu.:190.0  
##  Max.   :2026-01-01   Max.   :230.0

library(plotly)

# Function to color the line chart based on sales increase/decrease
get_line_color <- function(data) {
  color_vector <- c("blue")  # Default to blue for no change
  for (i in 2:length(data$sales)) {
    if (data$sales[i] > data$sales[i - 1]) {
      color_vector <- c(color_vector, "green")
    } else {
      color_vector <- c(color_vector, "red")
    }
  }
  return(color_vector)
}

# Get color vector for the plot
color_vector <- get_line_color(sales_data)

# Plotly plot
plot <- plot_ly(data = sales_data, x = ~date, y = ~sales, type = 'scatter', mode = 'lines+markers', 
                hoverinfo = 'text', text = ~paste('Sales: ', sales), line = list(color = color_vector)) %>%
  layout(title = 'Sales Escalation', xaxis = list(title = 'Date'), yaxis = list(title = 'Sales'))

# Display the plot
plot

# Save the plot as a PNG image
img_path <- "sales_plot.png"
export(plot, file = img_path)

## Warning: 'export' is deprecated.
## Use 'orca' instead.
## See help("Deprecated")

# Include the saved image in the report
knitr::include_graphics(img_path)